0 mg/cm3 lower than that in 50-59 year group, which accounts for nearly 30% decrease in vBMD with 40 years increase. Age-related vBMD changes in the head quadrants were similar to that in total. With age, the trend line correlation coefficients for vBMD in quadrants were relatively small, but significant (p less then 0.001) for all. The femoral head integral vBMD correlates well with neck vBMD and FN aBMD. FN aBMD explained 45% of head integral vBMD variance (p less then 0.0001). Elderly women had relative preservation of femoral head and neck bone volume from 50 yrs. over four decades but markedly lower integral vBMD of proximal femur. The findings of our study call in question about the concept of bone expansion with aging even in elderly age.Osteoporosis is a prevalent but underdiagnosed condition. As compared to dual-energy X-ray absorptiometry (DXA) measures, we aimed to develop a deep convolutional neural network (DCNN) model to classify osteopenia and osteoporosis with the use of lumbar spine X-ray images. Herein, we developed the DCNN models based on the training dataset, which comprising 1616 lumbar spine X-ray images from 808 postmenopausal women (aged 50 to 92 years). DXA-derived bone mineral density (BMD) measures were used as the reference standard. We categorized patients into three groups according to DXA BMD T-score normal (T ? -1.0), osteopenia (-2.5 less then T less then -1.0), and osteoporosis (T ? -2.5). T-scores were calculated by using the BMD dataset of young Chinese female aged 20-40 years as a reference. A 3-class DCNN model was trained to classify normal BMD, osteoporosis, and osteopenia. https://www.selleckchem.com/products/pirtobrutinib-loxo-305.html Model performance was tested in a validation dataset (204 images from 102 patients) and two test datasets (396 images from 198 patients and 348 images from 147 patients respectively). Model performance was assessed by the receiver operating characteristic (ROC) curve analysis. The results showed that in the test dataset 1, the model diagnosing osteoporosis achieved an AUC of 0.767 (95% confidence interval [CI] 0.701-0.824) with sensitivity of 73.7% (95% CI 62.3-83.1), the model diagnosing osteopenia achieved an AUC of 0.787 (95% CI 0.723-0.842) with sensitivity of 81.8% (95% CI 67.3-91.8); In the test dataset 2, the model diagnosing osteoporosis yielded an AUC of 0.726 (95% CI 0.646-0.796) with sensitivity of 68.4% (95% CI 54.8-80.1), the model diagnosing osteopenia yielded an AUC of 0.810 (95% CI, 0.737-0.870) with sensitivity of 85.3% (95% CI, 68.9-95.0). Accordingly, a deep learning diagnostic network may have the potential in screening osteoporosis and osteopenia based on lumbar spine radiographs. However, further studies are necessary to verify and improve the diagnostic performance of DCNN models.The aim of this study was to determine and compare risk factors associated with incident fractures in older adults with and without obesity, defined by both body mass index (BMI) and body fat percentage.
1,099 older adults (mean±standard deviation age=63.0±7.5) years, participated in this prospective cohort study. Obesity status at baseline was defined by BMI (?30kg/m) obtained by anthropometry and body fat percentage (?30% for men and?40% for women) assessed by dual-energy X-ray absorptiometry (DXA). Total hip and lumbar spine areal bone mineral density (aBMD) were assessed by DXA up to five years. Incident fractures were self-reported up to 10years.
Prevalence of obesity was 28% according to BMI and 43% according to body fat percentage. Obese older adults by BMI, but not body fat percentage, had significantly higher aBMD at the total hip and spine compared with non-obese (both p-value&lt;0.05). Obese older adults by body fat percentage had significantly higher likelihood of all incident fractures (Older adults at increased risk of incident fractures.
Obesity defined by body fat percentage is associated with increased likelihood of incident fractures in community-dwelling older adults, whereas those who are obese according to BMI have reduced likelihood of incident fracture which appears to be explained by higher aBMD. Falls risk assessment may improve identification of obese older adults at increased risk of incident fractures.Bone morphogenetic proteins (BMPs) were purified from demineralized bone matrix by their ability to induce new bone formation in vivo. BMPs represent a large sub-family of proteins structurally related to TGF-beta and activins. Two BMP bone graft substitutes, BMP2 (InFuse®) and BMP7 (OP1®) have been developed as products for the repair of long bone non-union fractures and lumbar spinal fusion in humans. The approval of BMP2 and BMP7 based products for use in the clinic supports that the signals responsible for bone formation at ectopic sites can form a basis as therapeutics for bone repair and regeneration. This article describes a historical perspective of the discovery BMPs.Osteogenesis imperfecta (OI) is commonly associated with short stature, but it is unclear whether this is exclusively secondary to fractures and bone deformities or whether there is a primary defect in longitudinal bone growth. As metacarpal and phalangeal bones are rarely affected by fractures and deformities, any length deficits in these bones should reflect a direct disease effect on longitudinal growth. This study therefore assessed the relationship of hand bone length with clinical OI type and genotype.
Prospective study.
The length of all 19 tubular hand bones were measured in 144 individuals (age 6 to 57years; 68 female) who had OI caused by COL1A1 or COL1A2 variants. Measurements of bone length were converted to z-scores using published reference data. Bone length was mostly normal in OI type I but was significantly decreased in OI types III and IV. Mean hand bone length z-score (i.e., the average length z-score of all 19 bones of a hand) was -0.2 for OI type I, -2.9 for OI type III and -1.2 for OI type IV. Mean hand bone length z-score was positively associated with height z-score (r=0.65, P&lt;0.001). Regarding genotype-phenotype correlations, mean hand bone length z-score was close to 0 in individuals with COL1A1 mutations leading to haploinsufficiency but were significantly lower in the presence of mutations leading to triple-helical glycine substitutions in either the alpha 1 or alpha 2 chain of collagen type I.
COL1A1 and COL1A2 mutations affect bone growth not only by inducing fractures and bone deformities, but also through longitudinal growth deficits in bones that do not fracture or deform.
COL1A1 and COL1A2 mutations affect bone growth not only by inducing fractures and bone deformities, but also through longitudinal growth deficits in bones that do not fracture or deform.